.png){kind=icon}
.png){kind=icon}
Return
Student performance prediction model using HLRO-DMN
.png){kind=icon}
PRE
AI
DOI：10.1504/IJBIC.2026.153410.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This research introduced the proposed hybrid leader remora optimisation algorithm with deep maxout network (HLRO_DMN) for accurately predicting the students' performance. Initially, the input data acquired from the dataset is transformed into a suitable format using Yeo-Johnson's transformation. Then, the dice coefficient is employed for selecting optimal features, which combines the feature score obtained from the Fisher score and the Tversky index. In addition, the data augmentation is completed by the bootstrapping method, and the performance prediction is carried out by the DMN, wherein the weight of the DMN is tuned by the HLRO algorithm. Besides, the experimentation of HLRO_DMN attained the best result using certain metrics, like mean square error (MSE), Root mean square error (RMSE), and mean absolute error (MAE), and the accuracy of the corresponding values noted by the devised scheme are 5.4032, 0.175, 0.4444, and 91.314, respectively.
Keywords:
remora optimisation algorithm
ROA
deep maxout network
hybrid leader-based optimisation
HLBO
Yeo-Johnson's transformation
dice coefficient
Journal
I
IF:
2
Papers:
15
Citations:
738